inst/models/shiny_sis.R

In epiworldRShiny: A 'shiny' Wrapper of the R Package 'epiworldR'

# alt-name: Network SIS

shiny_sis <- function(input) {

  # Creating model
  model_sis <- epiworldR::ModelSIS(
    name              = input$sis_disease_name,
    prevalence        = input$sis_prevalence,
    transmission_rate = input$sis_transmission_rate,
    recovery_rate     = input$sis_recovery_rate
    )

  # Creating network
  epiworldR::agents_smallworld(
      model_sis,
      n = input$sis_population_size,
      k = input$sis_k,
      d = as.logical(input$sis_directed),
      p = input$sis_prob_rewiring
  )

  # NPIs -----------------------------------------------------------------------
  interventions_add_all(model_sis, "sis", input)
  
  # Running and printing
  epiworldR::verbose_off(model_sis)
  epiworldR::run(model_sis, ndays = input$sis_n_days, seed = input$sis_seed)

  # Plot, summary, and reproductive number
  plot_sis <- function() plot_epi(model_sis)

  summary_sis <- function() summary(model_sis)

  reproductive_sis <- function() plot_reproductive_epi(model_sis)

  # Table
  table_sis <- function() {
    df <- as.data.frame(epiworldR::get_hist_total(model_sis))

    # Subset to only include "infection" state
    infection_data <- df[df$state == "Infected", ]

    # Row with the maximum count
    max_infection_row <- infection_data[which.max(infection_data$count), ]

    # Row number of the maximum count in the original data frame
    max_row_number <- which(
      df$date == max_infection_row$date & df$state == "Infected"
      )

    df[max_row_number,] <- sprintf(
      "<strong>%s</strong>",
      df[max_row_number,]
      )

    df
  }

  # Output list
  return(
    list(
      epicurves_plot     = plot_sis,
      reproductive_plot  = reproductive_sis,
      model_summary      = summary_sis,
      model_table        = table_sis
    )
  )

}

sis_panel <- function(model_alt) {

  shiny::conditionalPanel(
    simulate_button("sis"),
    condition = sprintf("input.model == '%s'", model_alt),
    text_input_disease_name("sis"),
    slider_prevalence("sis"),
    slider_input_rate("sis", "Transmission probability", 0.05, input_label = "transmission_rate"),
    slider_input_rate("sis", "Recovery probability (daily)", 0.14, input_label = "recovery_rate"),
    numeric_input_ndays("sis"),
    seed_input("sis"),
    network_input("sis"),
    npis_input("sis")
  )

}